Analysis¶
Data analysis pipeline including background removal, segmentation and fitting
@author: R. Patrick Xian
-
class
mpes.analysis.
BoundedArea
(image=None, shape=None, subimage=None)¶ Bounded area object from a parametric equation.
-
mask
¶ Subimage attribute as mask
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setBoundary
(pmz='linear', boundtype='>', **kwds)¶ Add bound to grid to redefine subgrid.
Parameters: - pmz : str | ‘linear’
Parametrization (pmz) of the decision boundary (‘linear’ or ‘circular’).
- boundtype : str | ‘>’
Bound region specification (‘>’ or ‘<’).
**kwds : keyword arguments
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subgrid
¶ Substituent pixel coordinates of the image.
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toMask
(inbound=1, exbound=0)¶ Generate a scaled mask from existing shape.
Parameters: - inbound : float | 1
Value for the pixels within the boundary.
- exbound : float | 0
Value for the pixels outside the boundary.
Return: - modmask : 2d array
Modified mask as a 2d array.
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view
(origin='lower', cmap='terrain_r', axes=True, **kwds)¶ Display the current mask.
Parameters: - origin : str | ‘lower’
Location of the image origin.
- cmap : str | ‘terrain_r’
Color map
- axes : bool | True
Axes visibility option in plot.
- **kwds : keyword arguments
Additional arguments for
matplotlib.pyplot.imshow()
.
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-
class
mpes.analysis.
EnergyCalibrator
(biases=None, files=[], folder=[], file_sorting=True, traces=None, tof=None)¶ Electron binding energy calibration workflow.
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addFeatures
(ranges, refid=0, traces=None, infer_others=False, mode='append', **kwds)¶ Select or extract the equivalent landmarks (e.g. peaks) among all traces.
Parameters: - ranges : list/tuple
Collection of feature detection ranges, within which an algorithm (i.e. 1D peak detector) with look for the feature.
- refid : int | 0
Index of the reference trace (EDC).
- traces : 2D array | None
Collection of energy dispersion curves (EDCs).
- infer_others : bool | True
Option to infer the feature detection range in other traces (EDCs) from a given one.
- mode : str | ‘append’
Specification on how to change the feature ranges (‘append’ or ‘replace’).
- **kwds : keyword arguments
Dictionarized keyword arguments for trace alignment (See
self.findCorrespondence()
)
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calibrate
(refid=0, ret=['coeffs'], **kwds)¶ Calculate the functional mapping between time-of-flight and the energy scale using optimization methods.
Parameters: - refid : int | 0
The reference trace index (an integer).
- ret : list | [‘coeffs’]
Options for return values (see
mpes.analysis.calibrateE()
).- **kwds : keyword arguments
See available keywords for
mpes.analysis.calibrateE()
.
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dup
¶ The duplication number.
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featureExtract
(ranges=None, traces=None, **kwds)¶ Select or extract the equivalent landmarks (e.g. peaks) among all traces.
Parameters: ranges : list | None traces : 2D array | None **kwds : keyword arguments
See available keywords in
mpes.analysis.peaksearch()
.
-
static
findCorrespondence
(sig_still, sig_mov, method='dtw', **kwds)¶ Determine the correspondence between two 1D traces by alignment.
Parameters: - sig_still, sig_mov : 1D array, 1D array
Input 1D signals.
- method : str | ‘dtw’
Method for 1D signal correspondence detection (‘dtw’ or ‘ptw’).
- **kwds : keyword arguments
See available keywords for the following functions, (1)
fastdtw.fastdtw()
(whenmethod=='dtw'
) (2)ptw.ptw.timeWarp()
(whenmethod=='ptw'
)
Return: - pathcorr : list
Pixel-wise path correspondences between two input 1D arrays (sig_still, sig_mov).
-
nfiles
¶ The number of loaded files.
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normalize
(**kwds)¶ Normalize the spectra along an axis.
Parameters: - **kwds : keyword arguments
See the keywords for
mpes.utils.normspec()
.
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nranges
¶ The number of specified feature ranges.
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ntraces
¶ The number of loaded/calculated traces.
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read
(form='h5', tracename='', tofname='ToF')¶ Read traces (e.g. energy dispersion curves) from files.
Parameters: - form : str | ‘h5’
Format of the files (‘h5’ or ‘mat’).
- tracename : str | ‘’
Name of the group/attribute corresponding to the trace.
- tofname : str | ‘ToF’
Name of the group/attribute corresponding to the time-of-flight.
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saveParameters
(form='h5', save_addr='./energy')¶ Save all the attributes of the workflow instance for later use (e.g. energy scale conversion).
Parameters: - form : str | ‘h5’
The file format to save the attributes in (‘h5’/’hdf5’ or ‘mat’).
- save_addr : str | ‘./energy’
The filename to save the files with.
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view
(traces, segs=None, ranges=None, peaks=None, show_legend=True, ret=False, display=True, backend='matplotlib', linekwds={}, linesegkwds={}, scatterkwds={}, legkwds={}, **kwds)¶ Display a plot showing line traces with annotation.
Parameters: - traces : 2d array
Matrix of traces to visualize.
segs : ranges : peaks : 2d array
Peak positions for labelling the traces.
- ret : bool
Return specification.
- backend : str | ‘matplotlib’
Backend specification, choose between ‘matplotlib’ (static) or ‘bokeh’ (interactive).
- linekwds : dict | {}
Keyword arguments for line plotting (see
matplotlib.pyplot.plot()
).- scatterkwds : dict | {}
Keyword arguments for scatter plot (see
matplotlib.pyplot.scatter()
).- legkwds : dict | {}
Keyword arguments for legend (see
matplotlib.pyplot.legend()
).- **kwds : keyword arguments
keyword data type meaning maincolor str labels list Labels for each curve xaxis 1d array x (horizontal) axis values title str Title of the plot legend_location str Location of the plot legend
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-
class
mpes.analysis.
Model
(func, xvar, name=None)¶ Class of fitting curve models
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fit
(data, inits, method='leastsq', **fitkwds)¶ Run the optimization
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model_eval
(params)¶ Evaluate the fitting model with given parameters
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static
normalize
(data)¶ Normalize n-dimensional data
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partial_eval
(params, part=0)¶ Evaluate parts of a composite fitting model
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class
mpes.analysis.
MomentumCorrector
(image, rotsym=6)¶ Momentum distortion correction and momentum calibration workflow.
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applyDeformation
(image, ret=True, **kwds)¶ Apply the deformation field to a specified image slice.
Parameters: - image : 2D array
Image slice to apply the deformation.
- ret : bool | True
Option to return the image after deformation.
- **kwds : keyword arguments
rdeform, cdeform: 2D array, 2D array | self.rdeform_field, self.cdeform_field Row- and column-ordered deformation fields. interp_order: int | 1 Interpolation order. others: See scipy.ndimage.map_coordinates()
.
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calcGeometricDistances
()¶ Calculate geometric distances involving the center and the vertices. Distances calculated include center-vertex and nearest-neighbor vertex-vertex distances.
-
calcSymmetryScores
(symtype='rotation')¶ Calculate the symmetry scores from geometric quantities.
Paramters: - symtype : str | ‘rotation’
Type of symmetry.
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calibrate
(image, point_from, point_to, dist, ret='coeffs', **kwds)¶ Calibration of the momentum axes. Obtain all calibration-related values, return only the ones requested.
Parameters: - image : 2d array
Image slice to construct the calibration function.
- point_from, point_to : list/tuple, list/tuple
Pixel coordinates of the two special points in (row, col) ordering.
- dist : float
Distance between the two selected points in inverse Angstrom.
- ret : str | ‘coeffs’
Specification of return values (‘axes’, ‘extent’, ‘coeffs’, ‘grid’, ‘func’, ‘all’).
- **kwds : keyword arguments
See arguments in
mpes.analysis.calibrateE()
.
Return: Specified calibration parameters in a dictionary.
-
coordinateTransform
(type, keep=False, ret=False, interp_order=1, mapkwds={}, **kwds)¶ Apply a pixel-wise coordinate transform to an image.
Parameters: - type : str
Type of deformation to apply to image slice.
- keep : bool | False
Option to keep the specified coordinate transform.
- ret : bool | False
Option to return transformed image slice.
- interp_order : int | 1
Interpolation order for filling in missed pixels.
- mapkwds : dict | {}
Additional arguments passed to
scipy.ndimage.map_coordinates()
.- **kwds : keyword arguments
Additional arguments in specific deformation field. See
symmetrize.sym
module.
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correct
(axis, use_composite_transform=False, update=False, use_deform_field=False, updatekwds={}, **kwds)¶ Apply a 2D transform to a stack of 2D images (3D) along a specific axis.
Parameters: - axis : int
Axis for slice selection.
- use_composite_transform : bool | False
Option to use the composite transform involving the rotation.
- update : bool | False
Option to update the existing figure attributes.
- use_deform_field : bool | False
Option to use deformation field for distortion correction.
- **kwds : keyword arguments
keyword data type meaning image 2d array 3D image for correction dfield list/tuple row and column deformation field warping 2d array 2D transform correction matrix
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featureExtract
(image, direction='ccw', type='points', center_det='centroidnn', symscores=True, **kwds)¶ - Extract features from the selected 2D slice.
- Currently only point feature detection is implemented.
Parameters: - image : 2d array
The image slice to extract features from.
- direction : str | ‘ccw’
The circular direction to reorder the features in (‘cw’ or ‘ccw’).
- type : str | ‘points’
The type of features to extract.
- center_det : str | ‘centroidnn’
Specification of center detection method (‘centroidnn’, ‘centroid’, None).
- **kwds : keyword arguments
Extra keyword arguments for
symmetrize.pointops.peakdetect2d()
.
-
features
¶ Dictionary of detected features for the symmetrization process.
self.features
is a derived attribute from existing ones.
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static
getWarpFunction
(**kwds)¶ Construct warping function to apply to other datasets. # TODO: turn this into a fully operational method.
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linWarpEstimate
(weights=(1, 1, 1), optfunc='minimize', optmethod='Nelder-Mead', ret=True, warpkwds={}, **kwds)¶ Estimate the homography-based deformation field using landmark correspondences.
Parameters: - weights : tuple/list/array | (1, 1, 1)
Weights added to the terms in the optimizer. The terms are assigned to the cost functions of (1) centeredness, (2) center-vertex symmetry, (3) vertex-vertex symmetry, respectively.
- optfunc, optmethod : str/func, str | ‘minimize’, ‘Nelder-Mead’
Name of the optimizer function and the optimization method. See description in
mpes.analysis.sym.refsetopt()
.- ret : bool | True
Specify if returning the corrected image slice.
- warpkwds : dictionary | {}
Additional arguments passed to
symmetrize.sym.imgWarping()
.- **kwds : keyword arguments
keyword data type meaning niter int Maximum number of iterations landmarks list/array Symmetry landmarks selected for registration fitinit tuple/list Initial conditions for fitting
Return: Corrected 2D image slice (when
ret=True
is specified in the arguments).
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resetDeformation
(**kwds)¶ Reset the deformation field.
-
rotate
(angle='auto', ret=False, **kwds)¶ Rotate 2D image in the homogeneous coordinate.
Parameters: - angle : float/str
Angle of rotation (specify ‘auto’ to use automated estimation).
- ret : bool | False
Return specification (True/False)
- **kwds : keyword arguments
keyword data type meaning image 2d array 2D image for correction center tuple/list pixel coordinates of the image center scale float scaling factor in rotation See
symmetrize.sym.sym_pose_estimate()
for other keywords.
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saveImage
(form='tiff', save_addr='./', dtyp='float32', **kwds)¶ Save the distortion-corrected dataset (image only, without axes).
Parameters: - form : str | ‘tiff’
File format for saving the corrected image (‘tiff’ or ‘mat’).
- save_addr : str | ‘./’
The address to save the file at.
- dtyp : str | ‘float32’
Data type (in case conversion if needed).
- **kwds : keyword arguments
See keywords from
tifffile.imsave()
.
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saveParameters
(form='h5', save_addr='./momentum')¶ Save all the attributes of the workflow instance for later use (e.g. momentum scale conversion, reconstructing the warping map function).
Parameters: - form : str | ‘h5’
File format to for saving the parameters (‘h5’/’hdf5’, ‘mat’)
- save_addr : str | ‘./momentum’
The address for the to be saved file.
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selectSlice2D
(selector, axis=2)¶ Select (hyper)slice from a (hyper)volume.
Parameters: - selector : slice object/list/int
Selector along the specified axis to extract the slice (image). Use the construct slice(start, stop, step) to select a range of images and sum them. Use an integer to specify only a particular slice.
- axis : int | 2
Axis along which to select the image.
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splineWarpEstimate
(image, include_center=True, fixed_center=True, iterative=False, interp_order=1, update=False, ret=False, **kwds)¶ Estimate the spline deformation field using thin plate spline registration.
Parameters: - image : 2D array
Image slice to be corrected.
- include_center : bool | True
Option to include the image center/centroid in the registration process.
- fixed_center : bool | True
Option to have a fixed center during registration-based symmetrization.
- iterative : bool | False
Option to use the iterative approach (may not work in all cases).
- interp_order : int | 1
Order of interpolation (see
scipy.ndimage.map_coordinates()
).- update : bool | False
Option to keep the spline-deformed image as corrected one.
- ret : bool | False
Option to return corrected image slice.
- **kwds : keyword arguments
landmarks: list/array | self.pouter_ord Landmark positions (row, column) used for registration. new_centers: dict | {} User-specified center positions for the reference and target sets. {‘lmkcenter’: (row, col), ‘refcenter’: (row, col)}
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symscores
¶ Dictionary of symmetry-related scores.
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static
transform
(points, transmat)¶ Coordinate transform of a point set in the (row, column) formulation.
Parameters: - points : list/array
Cartesian pixel coordinates of the points to be transformed.
- transmat : 2D array
The transform matrix.
Return: Transformed point coordinates.
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update
(content, **kwds)¶ Update specific attributes of the class.
Parameters: - content : str | ‘all’
‘feature’ = update only feature attributes ‘image’ = update only image-related attributes ‘all’ = update both feature and image-related attributes
- **kwds : keyword arguments
Extra keyword arguments passed into
self._featureUpdate()
.
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updateDeformation
(rdisp, cdisp, reset=False, **kwds)¶ Update the deformation field.
Parameters: - rdisp, cdisp : 2D array, 2D array
Row- and column-ordered displacement fields.
- reset : bool | False
Option to reset the deformation field.
- **kwds : keyword arguments
See
mpes.analysis.MomentumCorrector.resetDeformation()
.
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view
(origin='lower', cmap='terrain_r', figsize=(4, 4), points={}, annotated=False, display=True, backend='matplotlib', ret=False, imkwds={}, scatterkwds={}, **kwds)¶ Display image slice with specified annotations.
Parameters: - origin : str | ‘lower’
Figure origin specification (‘lower’ or ‘upper’).
- cmap : str | ‘terrain_r’
Colormap specification.
- figsize : tuple/list | (4, 4)
Figure size.
- points : dict | {}
Points for annotation.
- annotated : bool | False
Option for annotation.
- display : bool | True
Display option when using
bokeh
to render interactively.- backend : str | ‘matplotlib’
Visualization backend specification. :’matplotlib’: use static display rendered by matplotlib. :’bokeh’: use interactive display rendered by bokeh.
- ret : bool | False
Option to return figure and axis objects.
- imkwd : dict | {}
Keyword arguments for
matplotlib.pyplot.imshow()
.- **kwds : keyword arguments
General extra arguments for the plotting procedure.
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-
mpes.analysis.
affineWarping
(img, landmarks, refs, ret='image')¶ Perform image warping based on a generic affine transform (homography).
Parameters: - img : 2D array
Input image (distorted)
- landmarks : list/array
List of pixel positions of the
- refs : list/array
List of pixel positions of regular
Returns: - imgaw : 2D array
Image after affine warping.
- maw : 2D array
Homography matrix for the tranform.
-
mpes.analysis.
applyWarping
(imgstack, axis, hgmat)¶ Apply warping transform for a stack of images along an axis
Parameters: - imgstack : 3D array
Image stack before warping correction.
- axis : int
Axis to iterate over to apply the transform.
- hgmat : 2D array
Homography matrix.
Return: - imstack_transformed : 3D array
Stack of images after correction for warping.
-
mpes.analysis.
apply_mask_along
(arr, mask, axes=None)¶ Apply a mask in a low dimensional slice throughout a high-dimensional array.
Parameters: - arr : nD array
Multidimensional array for masking.
- mask : nD array
Mask to apply.
- axes : list/tuple of int | None
The axes to apply the mask to.
Return: - maskedarr : nD array
Masked multidimensional array.
-
mpes.analysis.
bandpath_map
(bsvol, pathr=None, pathc=None, path_coords=None, eaxis=2)¶ Extract band diagram map from volumetric data.
Parameters: - bsvol : 3D array
Volumetric band structure data.
- pathr, pathc : 1D array | None, None
Row and column pixel coordinates along the band path (ignored if path_coords is given).
- path_coords : 2D array | None
Combined row and column pixel coordinates of the band path.
- eaxis : int | 2
Energy axis index.
Return: - bpm : 2D array
Band path map sampled from the volumetric data.
-
mpes.analysis.
blocknorm
(data, mavg_axis=0, blockwidth=1)¶ Block-thresholding 2D data.
Parameters: - data : ndarray
Data to normalize.
- mavg_axis : int | 0
Axis to move the block along.
- blockwidth : int | 1
Width of the moving block.
Return: - datanorm : ndarray
Block-normalized data.
-
mpes.analysis.
bootstrapfit
(data, axval, model, params, axis=0, dfcontainer=None, pbar=False, pbenv='classic', ret='all', **kwds)¶ Line-by-line fitting via bootstrapping fitted parameters from one line to the next.
Parameters: - data : ndarray
Data used in fitting.
- axval : list/numeric array
Value for the axis.
- model : lmfit Model object
The fitting model.
- params : lmfit Parameters object
Initial guesses for fitting parameters.
- axis : int | 0
The axis of the data to fit.
- dfcontainer : pandas DataFrame | None
Dataframe container for the fitting parameters.
- pbar : bool | False
Progress bar condition.
- pbenv : str | ‘classic’
Progress bar environment (‘classic’ for generic version, ‘notebook’ for notebook compatible version).
- **kwds : keyword arguments
keyword data type meaning maxiter int maximum iteration per fit (default = 20) concat bool concatenate the fit parameters to DataFrame input False (default) = no concatenation, use an empty DataFrame to start True = with concatenation to input DataFrame bgremove bool toggle for background removal (default = True) flipped bool toggle for fitting start position (if flipped, fitting start from the last line) limpropagate bool verbose bool toggle for output message (default = False)
Returns: - df_fit : pandas DataFrame
Dataframe container populated with obtained fitting parameters.
- data_nobg : ndarray
Background-removed (Shirley-type) traces.
-
mpes.analysis.
build_dynamic_matrix
(fitparams, display_range=slice(None, None, None), pre_t0_range=slice(None, 1, None))¶ Construct the dynamic matrix from the fitting results: for each fitting parameter, construct time-dependent value, time-dependent absolute and relative changes
Parameters: - fitparams : 3D ndarray
fitting output
- display_range : slice object | slice(None, None, None)
display time range of the fitting parameters (default = full range)
- pre_t0_range : slice object | slice(None, 1, None)
time range regarded as before time-zero
Returns: - dyn_matrix : 4D ndarray
calculated dynamic matrix
-
mpes.analysis.
calibrateE
(pos, vals, order=3, refid=0, ret='func', E0=None, t=None, aug=1, method='lstsq', **kwds)¶ Energy calibration by nonlinear least squares fitting of spectral landmarks on a set of (energy dispersion curves (EDCs). This amounts to solving for the coefficient vector, a, in the system of equations T.a = b. Here T is the differential drift time matrix and b the differential bias vector, and assuming that the energy-drift-time relationship can be written in the form, E = sum_n (a_n * t**n) + E0
Parameters: - pos : list/array
Positions of the spectral landmarks (e.g. peaks) in the EDCs.
- vals : list/array
Bias voltage value associated with each EDC.
- order : int | 3
Polynomial order of the fitting function.
- refid : int | 0
Reference dataset index, varies from 0 to vals.size - 1.
- ret : str | ‘func’
Return type, including ‘func’, ‘coeffs’, ‘full’, and ‘axis’ (see below).
- E0 : float | None
Constant energy offset.
- t : numeric array | None
Drift time.
- aug : int | 1
Fitting dimension augmentation (1=no change, 2=double, etc).
Returns: - pfunc : partial function
Calibrating function with determined polynomial coefficients (except the constant offset).
- ecalibdict : dict
A dictionary of fitting parameters including the following, :coeffs: Fitted polynomial coefficients (the a’s). :offset: Minimum time-of-flight corresponding to a peak. :Tmat: the T matrix (differential time-of-flight) in the equation Ta=b. :bvec: the b vector (differential bias) in the fitting Ta=b. :axis: Fitted energy axis.
-
mpes.analysis.
calibrateK
(img, pxla, pxlb, k_ab=None, kcoorda=None, kcoordb=[0.0, 0.0], equiscale=False, ret=['axes'])¶ Momentum axes calibration using the pixel positions of two symmetry points (a and b) and the absolute coordinate of a single point (b), defaulted to [0., 0.]. All coordinates should be specified in the (row_index, column_index) format. See the equiscale option for details on the specifications of point coordinates.
Parameters: - img : 2D array
An energy cut of the band structure.
- pxla, pxlb : list/tuple/1D array
Pixel coordinates of the two symmetry points (a and b). Point b has the default coordinates [0., 0.] (see below).
- k_ab : float | None
The known momentum space distance between the two symmetry points.
- kcoorda : list/tuple/1D array | None
Momentum coordinates of the symmetry point a.
- kcoordb : list/tuple/1D array | [0., 0.]
Momentum coordinates of the symmetry point b (krow, kcol), default to k-space center.
- equiscale : bool | False
Option to adopt equal scale along both the row and column directions. :True: Use a uniform scale for both x and y directions in the image coordinate system. This applies to the situation where the points a and b are (close to) parallel with one of the two image axes. :False: Calculate the momentum scale for both x and y directions separately. This applies to the situation where the points a and b are sufficiently different in both x and y directions in the image coordinate system.
- ret : list | [‘axes’]
Return type specification, options include ‘axes’, ‘extent’, ‘coeffs’, ‘grid’, ‘func’, ‘all’.
Returns: - k_row, k_col : 1D array
Momentum coordinates of the row and column.
- axis_extent : list
Extent of the two momentum axis (can be used directly in imshow).
- k_rowgrid, k_colgrid : 2D array
Row and column mesh grid generated from the coordinates (can be used directly in pcolormesh).
-
mpes.analysis.
circmask
(img, rcent, ccent, rad, sign=1, ret='mask', **kwds)¶ Use a circular binary mask to cover an image.
Parameters: - img : 2D array
Input image to be masked.
- rcent : float
Row center position.
- ccent : float
Column center position.
- rad : float
Radius of circle.
- sign : int/str | 1
Value of the masked region (0, 1, ‘nan’ or ‘xnan’). ‘xnan’ means the masked region is 1 and the other region nan.
- ret : str | ‘mask’
Return type (‘mask’, ‘masked_image’)
- kwds : keyword arguments
keyword data type default meaning shape tuple/list shape of img see skimage.draw.circle() method str ‘graphic’ ‘graphic’ or ‘algebraic’ edgefactor float 1.02 prefactor to rad**2
Return: - cmask or cmask*img : 2D array
Mask only or masked image
-
mpes.analysis.
curvature2d
(image, cx=1, cy=1)¶ Implementation of 2D curvature calculation. The formula follows Zhang et al. Rev. Sci. Instrum. 82, 043712 (2011).
Parameters: - image : 2D array
2D image obtained from measurement.
- cx, cy : numeric, numeric | 1, 1
Scaling parameters in x and y directions.
-
mpes.analysis.
fitEllipseParams
(*coords, plot=False, img=None, **kwds)¶ Direct least-squares method for fitting ellipse from scattered points
-
mpes.analysis.
func_add
(*funcs)¶ Addition of an arbitray number of functions
*Parameters*
- *funcs : list/tuple
- functions to combine
*Returns*
- funcsum : function
- functional sum
-
mpes.analysis.
func_update
(func, suffix='')¶ Attach a suffix to parameter names and their instances in the expression of a function
*Parameters*
- func : function
- input function
- suffix : str | ‘’
- suffix to attach to parameter names
*Returns*
- params : list of str
- updated function parameters
- expr : str
- updated function expression
-
mpes.analysis.
gaussian
(feval=False, vardict=None)¶ Gaussian model
-
mpes.analysis.
gradn
(array, axes, **kwds)¶ Calculate nth-order gradients of the array along different directions.
Parameters: - array : numpy array
N-dimensional matrix for calculating the gradient.
- axes : int/list/tuple/1D array
A sequence of axes (from first to last) to calculate the gradient. When input a single integer, the gradient is calculated along that particular axis. For example, the 4th-order mixed gradient d4f/(dxdydxdy) requires the sequence (1, 0, 1, 0).
- **kwds : keyword arguments
See
numpy.gradient()
.
-
mpes.analysis.
peakdetect1d
(y_axis, x_axis=None, lookahead=200, delta=0)¶ Function for detecting local maxima and minima in a signal. Discovers peaks by searching for values which are surrounded by lower or larger values for maxima and minima respectively
Converted from/based on a MATLAB script at: http://billauer.co.il/peakdet.html
Parameters y_axis : list
A list containing the signal over which to find peaks- x_axis : list | None
- A x-axis whose values correspond to the y_axis list and is used in the return to specify the position of the peaks. If omitted an index of the y_axis is used.
- lookahead : int | 200
- distance to look ahead from a peak candidate to determine if it is the actual peak ‘(samples / period) / f’ where ‘4 >= f >= 1.25’ might be a good value
- delta : numeric | 0
- this specifies a minimum difference between a peak and the following points, before a peak may be considered a peak. Useful to hinder the function from picking up false peaks towards to end of the signal. To work well delta should be set to delta >= RMSnoise * 5.
Returns max_peaks : list
positions of the positive peaks- min_peaks : list
- positions of the negative peaks
-
mpes.analysis.
peakdetect2d
(img, method='daofind', **kwds)¶ Peak-like feature detection in a 2D image.
Parameters: - img : 2D array
Image matrix.
- method : str | ‘daofind’
Detection method (‘daofind’ or ‘maxlist’).
- **kwds : keyword arguments
Arguments passed to the specific methods chosen.
daofind: See astropy.stats.sigma_clipped_stats()
andphotutils.detection.DAOStarFinder()
.- sigma : float | 5.0
Standard deviation of the clipping Gaussian.
- fwhm : float | 3.0
FWHM of the convoluting Gaussian kernel.
- threshfactor : float | 8
Intensity threshold for background-foreground separation (foreground is above threshold).
maxlist: See skimage.feature.peak_local_max()
.- mindist : float | 10
Minimal distance between two local maxima.
- numpeaks : int | 7
Maximum number of detected peaks.
Return: - pks : 2D array
Pixel coordinates of detected peaks, in (column, row) ordering.
-
mpes.analysis.
peaksearch
(traces, tof, ranges=None, method='range-limited', pkwindow=3, plot=False)¶ Detect a list of peaks in the corresponding regions of multiple EDCs
Parameters: - traces : 2D array
Collection of EDCs.
- tof : 1D array
Time-of-flight values.
- ranges : list of tuples/lists | None
List of ranges for peak detection in the format [(LowerBound1, UpperBound1), (LowerBound2, UpperBound2), ….].
- method : str | ‘range-limited’
Method for peak-finding (‘range-limited’ and ‘alignment’).
- pkwindow : int | 3
Window width of a peak(amounts to lookahead in
mpes.analysis.peakdetect1d
).- plot : bool | False
Specify whether to display a custom plot of the peak search results.
Returns: - pkmaxs : 1D array
Collection of peak positions
-
mpes.analysis.
points2path
(pointsr, pointsc, ret='separated')¶ Calculate ordered pixel cooridnates along a path defined by specific intermediate points. The approach constructs the path using a set of line segments bridging the specified points, therefore it is also able to trace the sequence indices of these special points.
Parameters: - pointsr, pointsc : list/tuple/array
The row and column pixel coordinates of the special points along the sampling path.
- ret : str | ‘separated’
Specify if return combined (‘combined’) or separated (‘separated’) row and column coordinates.
Returns: - polyr, polyc : 1D array
Pixel coordinates along the path traced out sequentially.
- pid : 1D array
Pointwise indices of the special lpoints.
-
mpes.analysis.
rangeConvert
(x, xrng, pathcorr)¶ Convert value range using a pairwise path correspondence (e.g. obtained from time warping techniques).
Parameters: - x : 1D array
Values of the x axis (e.g. time-of-flight values).
- xrng : list/tuple
Boundary value range on the x axis.
- pathcorr : list/tuple
Path correspondence between two 1D arrays in the following form, [(id_1_trace_1, id_1_trace_2), (id_2_trace_1, id_2_trace_2), …]
Return: - xrange_trans : tuple
Transformed range according to the path correspondence.
-
mpes.analysis.
rectmask
(img, rcent, ccent, shift, direction='row', sign=1, ret='mask', **kwds)¶ Use a rectangular binary mask to cover an image.
Parameters: - img : 2D array
Input image to be masked
- rcent : float
Row center position
- ccent : float
Column center position
- shift : int/list of int
Pixel shifts
- direction : str | ‘row’
Direction to apply the shift to, ‘row’ or ‘column’ indicates row-wise or column-wise shift for generating the rectangular mask
- sign : int/str | 1
Value of the masked region (0, 1, ‘nan’ or ‘xnan’). ‘xnan’ means the masked region is 1 and the other region nan.
- ret : str | ‘mask’
Return type (‘mask’, ‘masked_image’)
**kwds : keyword arguments
Return: - cmask or cmask*img : 2D array
Mask only or masked image
-
mpes.analysis.
regionExpand
(mask, **kwds)¶ Expand the region of a binarized image around a line position
Parameters mask : numeric binarized 2D array
the mask to be expanded- **kwds : keyword arguments
keyword data type meaning method str method of choice (‘offset’, ‘growth’) value numeric value to be assigned to the masked linecoords 2D array contains x and y positions of the line axoffsets tuple/list [downshift upshift] pixel number clipbounds tuple/list bounds in the clipping direction selem ndarray structuring element
Return mask : numeric 2D array
modified mask (returns the original mask if insufficient arguments are provided for the chosen method for region expansion)
-
mpes.analysis.
ridgeDetect
(mask, method='mask_mean_y', **kwds)¶ Detect the band ridges using selected methods.
Parameters
- mask : numeric 2D array
- the 2D integer-valued mask with labeled bands
- method : str
- the method used for ridge detection ‘mask_mean_y’ : mean mask position along y direction (default) ‘mask_mean_x’ : mean mask position along x direction
- **kwds : keyword arguments
keyword data type meaning x int/float x axis coordinates y int/float y axis coordinates
Return
- ridges : list of dataframes
- the ridge coordinates
-
mpes.analysis.
segment2d
(img, nbands=1, **kwds)¶ Electronic band segmentation using local thresholding and connected component labeling
Parameters
- img : 2D numeric array
- the 2D matrix to segment
- nbands : int
- number of electronic bands
**kwds : keyword arguments
Return
- imglabeled : 2D numeric array
- labeled mask
-
mpes.analysis.
shirley
(x, y, tol=1e-05, maxiter=20, explicit=False, warning=False)¶ Calculate the 1D best Shirley-Proctor-Sherwood background S for a dataset (x, y). A. Proctor, P. M. A. Sherwood, Anal. Chem. 54 13 (1982). The function is adapted from Kane O’Donnell’s routine 1. Finds the biggest peak 2. Use the minimum value on either side of this peak as the terminal points of the Shirley background. 3. Iterate over the process within maximum allowed iteration (maxiter) to reach the tolerance level (tol).
Parameters
- x : 1D numeric array
- the photoelectron energy axis
- y : 1D numeric array
- the photoemission intensity axis
- tol : float | 1e-5
- fitting tolerance
- maxiter : int | 20
- maximal iteration
- explicit : bool | False
- explicit display of iteration number
- warning : bool | False
- display of warnings during calculation
Return
- sbg : 1D numeric array
- Calculated Shirley background
-
mpes.analysis.
shirley2d
(x, y, tol=1e-05, maxiter=20, explicit=False, warning=False)¶ 2D Shirley background removal
-
mpes.analysis.
vertexGenerator
(center, fixedvertex=None, cvd=None, arot=None, nside=None, direction=-1, scale=1, diagdir=None, ret='all', rettype='float32')¶ Generation of the vertices of symmetric polygons.
Parameters: - center : (int, int)
Pixel positions of the symmetry center (row pixel, column pixel).
- fixedvertex : (int, int) | None
Pixel position of the fixed vertex (row pixel, column pixel).
- cvd : numeric | None
Center-vertex distance.
- arot : float | None
Spacing in angle of rotation.
- nside : int | None
The total number of sides for the polygon.
- direction : int | -1
Direction of angular rotation (1 = counterclockwise, -1 = clockwise)
- scale : float | 1
Radial scaling factor.
- diagdir : str | None
Diagonal direction of the polygon (‘x’ or ‘y’).
- ret : str | ‘all’
Return type. Specify ‘all’ returns all vertices, specify ‘generated’ returns only the generated ones (without the fixedvertex in the argument).
Return: - vertices : 2D array
Collection of generated vertices.
-
mpes.analysis.
voigt
(feval=False, vardict=None)¶ Voigt model


Cancer Regulome Datasets
Datasets
Before publishing any work using this data, please consult the TCGA Publication Guidelines
| Tumor Type | TCGA ID | Dataset Date | Regulome Explorer Datasets |
|---|---|---|---|
| Adrenocortical Carcinoma | ACC | 05/09/16 | Cancer Cell Manuscript |
| Breast | BRCA | 02/03/13 | Nature Manuscript | Latest |
| Bladder | BLCA | 05/20/13 | Nature Manuscript |
| Colorectal | COADREAD | 04/12/12 | Nature Manuscript |
| Endometrial | UCEC | 06/28/13 | Latest |
| Esophageal Carcinoma, with Gastric | ESCASTAD | 1/12/17 | Nature Manuscript |
| Gastric | STAD | 07/23/14 | Nature Manuscript |
| Gliobastoma | GBM | 02/03/13 | Nature Manuscript |
| Kidney - Clear Cell | KIRC | 10/01/12 | Latest |
| Lower Grade Glioma | LGG | 6/25/15 | NEJM Manuscript |
| Lung Adenoma | LUAD | 02/03/13 | Latest |
| Lung Squamous | LUSC | 02/03/13 | Latest |
| Ovarian | OV | 02/03/13 | Latest |
| Prostate | PRAD | 11/5/15 | Cell Manuscript |
| Thyroid | THCA | 10/18/14 | Cell Manuscript |
| Uterine | UCEC | 02/03/13 | Latest |


Time visualisation library
Time visualisation library
The aim of this piece of software is to allow you to import any data you wish which contains longitude latitude and time values, for it to then be represented as heatpoints for the data creating a heatmap. For further look into how to achieve this check the out the documentation dropdown box at the top of the screen.Below you will find a live example that you can try using.


APSVASTMediaBuilderPlugin Class Reference
| Inherits from | NSObject |
|---|---|
| Conforms to | APSMediaBuilderPlugin |
| Declared in | APSVASTMediaBuilderPlugin.h APSVASTMediaBuilderPlugin.m |
Overview
The VAST Media Builder plugin extends an APSMediaBuilder
object by inserting preroll, postroll, midroll and non-linear ad breaks, loaded from VAST 1.0, VAST 2.0, VAST 3.0 and VMAP 1.0 sources.
Use the [APSMediaBuilder addPlugin:]
method to attach an APSVASTMediaBuilderPlugin
object to a media builder.
Configuration of the plugin can be done in three ways:
- programatically configure the plugin object, using available properties;
- configure the plugin object directly, using a dictionary - see
[APSMediaBuilderPlugin configureFromDictionary:]
; - configure the plugin with JSON. This is done as part of the
APSMediaBuilder
object configuration from JSON or from a dictionary. If the supplied configuration dictionary or interpreted JSON dictionary contains the “ads” key, then the corresponding value dictionary for that key will be provided to the plugin for configuration.
Plugin Dictionary Configuration
– dictionaryConfigurationKey
The string key that will be used in the root of JSON and dictionary builder configurations to hold all the specific configuration details for the plugin.
- (NSString *)dictionaryConfigurationKey
Return Value
String key.
Discussion
The string key that will be used in the root of JSON and dictionary builder configurations to hold all the specific configuration details for the plugin.
Declared In
APSMediaBuilderPlugin.h
– configureFromDictionary:
This method is called by the APSMediaBuilder
object, in the process of its configuration. The method is only called if in the root of the configuring JSON or NSDictionary
there is a key equal to the string defined by the plugin using the dictionaryConfigurationKey
method.
- (void)configureFromDictionary:(NSDictionary *)adConfiguration
Parameters
configuration |
A |
|---|
Discussion
This method is called by the APSMediaBuilder
object, in the process of its configuration. The method is only called if in the root of the configuring JSON or NSDictionary
there is a key equal to the string defined by the plugin using the dictionaryConfigurationKey
method.
Declared In
APSMediaBuilderPlugin.h
– startForBuilder:
This method will be called at the beginning of the mediaUnits
method of the APSMediaBuilder
object that registered the plugin.
- (void)startForBuilder:(APSMediaBuilder *)builder
Parameters
builder |
A reference to the calling |
|---|
Discussion
This method will be called at the beginning of the mediaUnits
method of the APSMediaBuilder
object that registered the plugin.
Declared In
APSMediaBuilderPlugin.h
Configuring Ad Breaks
vmapURL
The VMAP 1.0 URL that should be used to configure ad breaks.
@property (nonatomic) NSURL *vmapURL
Discussion
The VMAP 1.0 URL that should be used to configure ad breaks.
Declared In
APSVASTMediaBuilderPlugin.h
adBreakTemplate
The template object that holds default parameter values for ad breaks created from VMAP or VAST.
@property (nonatomic) APSVASTAdBreak *adBreakTemplate
Discussion
The template object that holds default parameter values for ad breaks created from VMAP or VAST.
Declared In
APSVASTMediaBuilderPlugin.h
adBreaks
The currently defined ad breaks that the plugin will try to fill.
@property (nonatomic) NSArray<APSVASTAdBreak*> *adBreaks
Discussion
The currently defined ad breaks that the plugin will try to fill.
Declared In
APSVASTMediaBuilderPlugin.h
Other Configurations
defaultAdBreakPositions
When loading ads via VMAP and using ad break positioning via “#”, this array defines the available, pre-defined positions that ads can be inserted at.
@property (nonatomic) NSArray *defaultAdBreakPositions
Discussion
When loading ads via VMAP and using ad break positioning via “#”, this array defines the available, pre-defined positions that ads can be inserted at.
Positions are defined using strings that can either specify absolute time intervals in seconds (e.g.: @“10”) or percentual values, relative to the full playback time (e.g.: @“50%”). Defaults to @[@"5", @"75%"]
.
Declared In
APSVASTMediaBuilderPlugin.h
desiredAdCategories
List of desired ad categories. Encoded with IABN-N values as specified in the “Content Categories” list of AdCOM 1.0.
@property (nonatomic) NSArray<NSString*> *desiredAdCategories
Discussion
List of desired ad categories. Encoded with IABN-N values as specified in the “Content Categories” list of AdCOM 1.0.
Declared In
APSVASTMediaBuilderPlugin.h
adBreakDelegate
Delegate for APSVASTAdBreak instances generated by this builder
@property (nonatomic, weak) id<APSVASTAdBreakDelegate> _Nullable adBreakDelegate
Discussion
Delegate for APSVASTAdBreak instances generated by this builder
Declared In
APSVASTMediaBuilderPlugin.h


FoodieFun is a platform built for you! We know that there's
no place for foodies like you to quickly and easily capture
what matters most from your Food Adventures.
Our goal is to help you get ita all - the photos, your
impressions, your ratings - all in to one convenient
location on the FoodieFun platform.
Using FoodieFun is as simple as ordering from a familiar
menu - just add the restaurants you're visiting and the
delicious food and drinks you're enjoying while there. Long
wait times? No problem - capture every detail with FoodieFun
while you wait. Spending quality time and only want to make
a quick note? We've got your back - you can always come back
to a restaurant or menu item you've added and fill in
missing details or edit what's there!
The Team
Ifiok Menadio
Back-End Developer
Oluwajoba Bello
Back-End Developer
Gurpreet Dhaliwal
Front-End Developer
Blake Lower
Front-End Developer
Marlon Del Rosario
UI Developer
Josh Armantrout
UX / UI Designer
Kyla Sweeney
Project Lead
About Us
FoodieFun
Your Food Adventures...
As told by you
FoodieFun is a platform built for you! We know that there's no
place for foodies like you to quickly and easily capture what
matters most from your Food Adventures.
Our goal is to help you get ita all - the photos, your
impressions, your ratings - all in to one convenient location on
the FoodieFun platform.
Using FoodieFun is as simple as ordering from a familiar menu -
just add the restaurants you're visiting and the delicious food
and drinks you're enjoying while there. Long wait times? No
problem - capture every detail with FoodieFun while you wait.
Spending quality time and only want to make a quick note? We've
got your back - you can always come back to a restaurant or menu
item you've added and fill in missing details or edit what's
there!


The requester, <%= receiverNickname %>, initially said they received it from you but now have corrected that to say they haven't received it after all.
For request details and to communicate with <%= receiverNickname %>, go to <%= requestURL %>.


--- layout: post title: Rant against 3D date: 2006-05-11 00:00:00 categories: digital-media interfaces-&-interaction ---
Rant against 3D: "3-D isn’t an interface paradigm. 3-D isn’t a world model. 3-D isn’t the missing ingredient. 3-D isn’t an inherently better representation for every purpose. 3-D is an attribute, like the color blue. Any time you read or hear about how great 3-D is and how it’s going to change everything about computers and services, substitute the word blue for 3-D"
Hear, hear. My favourite recent example of this was a guy at a recent mobile games event I attended, talking about mobile games his company had been producing. He proffered the handy statistic that 3D games sold the same amount as 2D games, but cost about twice as much to produce.
So... err.... why bother doing them then?
Along with 3D I'd also put the word "interactive" into the box of magical pixie dust words that get sprinkled onto crap in an attempt to make it interesting and appealing. Interactivity in and of itself isn't a priori a good thing. I think the video and film production industry might be learning this lesson, when balancing production costs for truly interactive narratives against the payback for doing them well.


Manual
- Hit play button
- Click on Keyframes -> Zoom in
- Click on Zoom out Button -> Zoom out
- Click on Timeline -> Scroll (keep Zoomfactor)
- Reload -> Resets the video player
Color Codes
- Blue: Timestamps for orientation
- Green: Reference to Keyframes on Timeline
- Red: Current playback position


scor:SupplyChain rdf:type owl:Class ; rdfs:label "SupplyChain"@en; rdfs:comment "A Supply Chain is a ..."@en ; rdfs:label "Lieferkette"@de; rdfs:comment "Eine Lieferkette ist<EMAIL_ADDRESS>approach should be followed with all the elements starting from the basics ones like rdfs:label and rdfs:comment but also for the external annotation properties (i.e. skos:prefLabel).


Welcome to Joe's Projects!
Here you will find my various electronic design and hardware hacking
projects. Please click on the links to your left or read below.
The Videos
Click to here to
view my YouTube Channel
The Projects
You can build a simple pressure mat using an aluminum foil and paper
towel capacitor. With this Arduino sketch it can triggle a Halloween
sequence...
After thinking about building a relay-computer for a while, I started
focusing on the flip-flop design and came up with idea for a master-slave
flip-flop with a capacitor-based latch for the master side and a hold
resistor based latch for the slave side. Of course I wanted to try it, so I
designed a 4-bit counter...
Long ago I tried to start a business and wanted to accept credit card
transactions, so I leased a credit card terminal from CardService
International. The business went nowhere, but I leased this
machine long enough so that eventually I came to own it. I'm sure it
cost me at least $1500 in lease payments...
Delay line memory always fascinated me. It is well known that early
computers such as the UNIVAC-I
used mercury acoustic delay line memory. Less well known is that the
use of digital delay line memory continued well into the semiconductor era
in the form of Torsional
and Quartz delay line memories for calculators...
This started out as a check on some performance problems which were fixed
for the latest version of JOE (version 4.3), but is interesting in its own
right as a comparison between some text editors...
MPL is a PL/I-like language for Motorola's 8-bit
M6800 development system, the
Motorola Exorciser (running MDOS). I assume it's Motorola's answer to PL/M for
Intel's 8080 microprocessor. The language seems to have been created before 1976.
It's fun to pretend it's 1978 and try to write some programs with this
language...
|


Jennie Gow's panel show appearances
Jennie Gow (born 1977-05-20[ref]) has appeared in 13 episodes between 2012-2016. Jennie Gow on Wikipedia.
- 2016-07-09 / Fighting Talk
- 2016-04-16 / Fighting Talk
- 2016-01-16 / Fighting Talk
- 2015-08-29 / Fighting Talk
- 2015-04-25 / Fighting Talk
- 2015-03-14 / Fighting Talk
- 2015-01-31 / Fighting Talk
- 2014-08-16 / Fighting Talk
- 2014-03-22 / Fighting Talk
- 2014-01-25 / Fighting Talk
- 2013-09-14 / Fighting Talk
- 2013-03-09 / Fighting Talk
- 2012-11-10 / Fighting Talk


{% extends 'indabom/base.html' %} {% block content %}
The requested URL was not found on this server.
Return to homepage


What's behind Labour's plan to overhaul BT and the British broadband network?
REUTERS: Britain's opposition Labour Party says if it wins the Dec. 12 election it will nationalise BT's broadband network and provide free internet for all within a decade, a radical election pledge to roll back decades of private ownership.
WHAT LED TO THIS?
The UK's biggest broadband and mobile phone provider was the flagship of Conservative Prime Minister Margaret Thatcher's policy of selling state-owned assets, a political revolution that she said would improve efficiency and "spread the nation's wealth among as many people as possible".
The 1984 privatisation was three times oversubscribed and made millions of Britons shareholders for the first time.
But it failed to trigger much competition as the company kept its grip on the British telecoms market until the widespread adoption of mobile in the nineties and home broadband in the following decade.
Regulator Ofcom made BT "unbundle" parts of its network, and the company created a fixed-line network arm called Openreach in 2006 to offer access to competitors. That led to pay-TV company Sky and new entrants like TalkTalk becoming major broadband providers.
WHY DOES THE BROADBAND NETWORK NEED OVERHAULING?
Customers, rivals and the regulator had criticised BT for poor service and a lack of ambition in upgrading its network to fibre, where fibre optic cable instead of copper is used to connect homes to the network. It has also spent billions of pounds on sports rights.
A full-fibre network could offer speeds of 1 Gigabit a second.
Some 95 per cent of premises have access to speeds above 30mbps through BT's hybrid copper-fibre roll-out but some homes and businesses - known collectively as premises - in rural areas and in some pockets in major cities, have internet speeds of less than 10 Mbps on BT's ageing copper connections.
Fewer than 10 per cent of British premises have access to full-fibre broadband, according to Ofcom, far behind countries like Spain, Europe's fibre leader, which plans to have 100 per cent 300 Mbps fibre broadband by 2021.
WHAT ARE THE ALTERNATIVES TO LABOUR'S PLAN?
Conservative Prime Minister Boris Johnson has promised full-fibre broadband to all homes by 2025.
BT CEO Philip Jansen aims to recast the company as the national champion the country needs to upgrade communications for consumers and businesses.
He said BT is ready to accelerate a fibre broadband roll-out that reaches a home or a business every 26 seconds, as long as the government and the regulator create conditions that allow it to make a fair return.
But he said last month Johnson's pledge would be "extremely difficult" to achieve.
BT's full-fibre network will reach 4 million premises by 2021, and it has plans to reach 15 million by the mid-2020s if it stacks up commercially.
BT's cable competitor Virgin Media, owned by Liberty Media, is building out its ultrafast network. Investors including Australia's Macquarie Group are backing new network companies.
WHAT ARE THE FLAWS OF LABOUR'S PLAN?
Labour is lagging the Conservatives in opinion polls and investors do not expect Labour to win the election, but the proposal creates unwelcome uncertainty for BT, its shareholders and rivals, which use the Openreach network.
Deutsche Telekom, which has a stake of just over 12 per cent, declined to comment.
Analysts questioned whether the estimated budget of £20 billion (US$25.6 billion) was realistic.
Costs in densely populated towns and cities have fallen to £300-400 per premise.
However, capital expenditure per premise passed can skyrocket to close to £2,500 in more rural areas, Neil Campling, analyst at Mirabaud said.
Tackling the final 10 per cent of predominantly rural premises may require "additional funding" of up to £5 billion to support commercial investment, he said.


import {NestedLayerComponent} from 'leaflet.control.nestedlayers/src/Leaflet.Control.NestedLayers.js'
NestedLayerComponent
Extends:
React Component to represent a single NestedLayer
Static Member Summary
| Static Public Members | ||
| public static |
Describes the React props object |
Constructor Summary
| Public Constructor | ||
| public |
constructor(props: object) |
Member Summary
| Public Members | ||
| public |
Returns a data URL suitable for use in a the src of an that contains the base 64 encoded layer swatch |
|
| public |
state: {} |
|
| public |
Calls the toggle selected state handler passed by the parent to change the selected state of the layer |
Method Summary
| Public Methods | ||
| public |
render(): Component Renders the component |
Public Constructors
public constructor(props: object) source
Params:
| Name | Type | Attribute | Description |
| props | object | Component properties |
|
| props.layer | NestedLayer | The layer to represent |
|
| props.onToggleSelected | function | The callback to change the layer's selected state |
Public Members
public getSwatch: string: * source
Returns a data URL suitable for use in a the src of an that contains the base 64 encoded layer swatch
public state: {} source
public toggleSelected: * source
Calls the toggle selected state handler passed by the parent to change the selected state of the layer


Gateway
BOT
15-Sep-18 04:50 PM
[dscrdeql] <Buddz1993> lmfaoo
[dscrdeql] <Buddz1993> Guys do we have any plans or have we contacted CMC to be listed there or are we waiting to be ranked higher in order to get more visibility??
[dscrdeql] <Buddz1993> just wondering
[dscrdeql] <AndronicusBass-Founder> More visibility
[dscrdeql] <Buddz1993> I see
[dscrdeql] <Buddz1993> makes totally sense.
[dscrdeql] <Buddz1993> even if we get listed in CMC we would be ranked in the 1000's what goood would that do for us
[dscrdeql] <Buddz1993> since nobody would care anyway
[dscrdeql] <SwimedDragon> Well, if we get other news, new exchanges or the first build, it will probably go up a lot. Plus there's not that much sell orders. And we still don't have many people in here. It probably takes time but I must say it's looking good so far
[dscrdeql] <Buddz1993> ye I know the price that we see today is a steal thats for sure
[dscrdeql] <Buddz1993> even when I bought yesterday I felt that seeing what price we used to have in the ICO
[dscrdeql] <! ariewayq ! The Real Human>
[dscrdeql] <! ariewayq ! The Real Human> @Jellysmash yeah i hope so my brotherhood
[dscrdeql] <SwimedDragon> And it's funny, it's even been under 1 sat I think
[dscrdeql] <Buddz1993> Really
[dscrdeql] <Buddz1993> never saw that low
[dscrdeql] <Buddz1993> the lowest I saw was 10 sats or so
[dscrdeql] <SwimedDragon> I did bought some at 4 sats I think
[dscrdeql] <SwimedDragon> Not sure at what prices I did buy on exrates
[dscrdeql] <Buddz1993> I wished and thinking when I bought yesterday was an amazing deal
[dscrdeql] <Buddz1993>
[dscrdeql] <Buddz1993> bought at 15 sats or so


This intention converts a boolean comparison expression
not(a negop b)
into
a op b
. Where
op
and
negop
are mutually inverse comparison operators, like
==
and
!=
.


---
description: This Container profile specification presents a representation of a Container
such as Docker or Singularity that is used in scientific compute.
edit_url: https://github.com/openschemas/specifications/blob/master/_specifications/ContainerRecipe.html
gh_folder: https://github.com/openschemas/specifications/tree/master/_specifications
gh_tasks: https://github.com/openschemas/specifications/labels/type%3A%20ContainerRecipe
hierarchy:
- Thing
- CreativeWork
- SoftwareSourceCode
mapping:
- bsc_description: ''
cardinality: MANY
controlled_vocab: ''
description: An alias for the item.
example: ''
expected_types:
- Text
marginality: Recommended
parent: Thing
property: alternateName
type: ''
type_url: ''
- bsc_description: Type of software e.g. database, tool, service. **Note:** Bioschemas
have removed [URL](http://schema.org/URL) from the Expected Types.
cardinality: MANY
controlled_vocab: Please indicate the container technology in all lowercase
description: Type of software application, e.g. 'Game, Multimedia'.
example: '{ "@type": ["SoftwareApplication", "Container"], "applicationCategory":
"singularity" }'
expected_types:
- Text
marginality: Optional
parent: ContainerRecipe
property: applicationCategory
type: ''
type_url: ''
- bsc_description: The command for a ContainerRecipe is typically a set of extra arguments
that are added to the entrypoint in a default running scenario
cardinality: MANY
controlled_vocab: ''
description: The command for a ContainerRecipe is typically a set of extra arguments
that are added to the entrypoint in a default running scenario
example: ''
expected_types:
- Text
marginality: Recommended
parent: ContainerRecipe
property: brands
type: ''
type_url: ''
- bsc_description: a special list of keywords to describe the underlying technologies
(e.g., Docker) that the container recipe affords building.
cardinality: MANY
controlled_vocab: ''
description: a special list of keywords to describe the underlying technologies
(e.g., Docker) that the container recipe affords building.
example: ''
expected_types:
- Text
marginality: Recommended
parent: ContainerRecipe
property: brands
type: ''
type_url: ''
- bsc_description: Publication about this software.
cardinality: MANY
controlled_vocab: ''
description: A citation or reference to another creative work, such as another publication,
web page, scholarly article, etc.
example: '{ "@type": ["SoftwareApplication", "Tool"], "citation": "https://dx.doi.org/10.1016/S0022-2836(05)80360-2"
}'
expected_types:
- CreativeWork
- Text
marginality: Recommended
parent: CreativeWork
property: citation
type: ''
type_url: ''
- bsc_description: A ContainerImage is the binary or associated shasum that references
a particular grouping of layers to form a complete images.
cardinality: ONE
controlled_vocab: ''
description: A ContainerImage is the binary or associated shasum that references
a particular grouping of layers to form a complete images.
example: ''
expected_types:
- ContainerImage
marginality: Recommended
parent: ContainerRecipe
property: ContainerImage
type: ''
type_url: ''
- bsc_description: ''
cardinality: ONE
controlled_vocab: ''
description: The date on which the CreativeWork was created or the item was added
to a DataFeed.
example: '{ "@type": ["SoftwareApplication", "Tool"], "dateCreated": "2108-06-13"
}'
expected_types:
- Date
- DateTime
marginality: Optional
parent: CreativeWork
property: dateCreated
type: ''
type_url: ''
- bsc_description: ''
cardinality: ONE
controlled_vocab: ''
description: The date on which the CreativeWork was most recently modified or when
the item's entry was modified within a DataFeed.
example: '{ "@type": ["SoftwareApplication", "Tool"], "dateModified": "2108-08-07"
}'
expected_types:
- Date
- DateTime
marginality: Optional
parent: CreativeWork
property: dateModified
type: ''
type_url: ''
- bsc_description: a linux container
cardinality: ONE
controlled_vocab: ''
description: A description of the item.
example: ''
expected_types:
- Text
marginality: Minimum
parent: Thing
property: description
type: ''
type_url: ''
- bsc_description: ''
cardinality: MANY
controlled_vocab: ''
description: If the file can be downloaded, URL to download the binary. For containers
without an exposed wget url or similar, this can be the pull command.
example: '{ "@type": ["SoftwareApplication", "Tool"], "downloadURL": "https://github.com/sacdallago/cellmap"
}'
expected_types:
- URL
marginality: Optional
parent: ContainerRecipe
property: downloadUrl
type: ''
type_url: ''
- bsc_description: The entrypoint is the executable that is called within the container
when the user executes it, typically via a "run" or equivalent command.
cardinality: MANY
controlled_vocab: ''
description: The entrypoint is the executable that is called within the container
when the user executes it, typically via a "run" or equivalent command.
example: ''
expected_types:
- Text
marginality: Recommended
parent: ContainerRecipe
property: entrypoint
type: ''
type_url: ''
- bsc_description: Please choose values from [EDAM:Operation](http://edamontology.org/operation_0004).
cardinality: MANY
controlled_vocab: '[EDAM:Operation](http://edamontology.org/operation_0004)'
description: Features or modules provided by this application (and possibly required
by other applications).
example: '{ "@type": ["SoftwareApplication", "Tool"], "featureList": ["http://edamontology.org/topic_0821",
"http://edamontology.org/topic_0128"] }'
expected_types:
- Text
- URL
marginality: Minimum
parent: ContainerRecipe
property: featureList
type: ''
type_url: ''
- bsc_description: Used to describe tools included into suites/bundles/workflows.
cardinality: MANY
controlled_vocab: ''
description: Indicates an item or CreativeWork that is part of this item, or CreativeWork
(in some sense).
example: ''
expected_types:
- CreativeWork
marginality: Optional
parent: CreativeWork
property: hasPart
type: ''
type_url: ''
- bsc_description: A help section is not present for all container technologies, but
is typically a blurb of text that can be shown to the user to give detais about
runtime usage or similar.
cardinality: ONE
controlled_vocab: ''
description: A help section is not present for all container technologies, but is
typically a blurb of text that can be shown to the user to give detais about runtime
usage or similar.
example: ''
expected_types:
- Text
marginality: Recommended
parent: ContainerRecipe
property: help
type: ''
type_url: ''
- bsc_description: Unique identifier for the container, typically corresponding to
a uri to designate a registry source
cardinality: ONE
controlled_vocab: ''
description: The identifier property represents any kind of identifier for any kind
of Thing, such as ISBNs,
GTIN codes, UUIDs etc. Schema.org provides dedicated properties for representing
many of these, either as textual strings or as URL (URI) links. See background
notes for more details.
example: /:@
expected_types:
- PropertyValue
- Text
- URL
marginality: Recommended
parent: Thing
property: identifier
type: ''
type_url: ''
- bsc_description: The input format of the data. Must be one of the [EDAM:Data](http://edamontology.org/data_0006)
concept labels or one of its synonyms.
cardinality: MANY
controlled_vocab: '[EDAM:Data](http://edamontology.org/data_0006)'
description: 'The input format of the data. Must be one of the [EDAM:Data](http://edamontology.org/data_0006)
concept labels or one of its synonyms.'
example: '{ "@type": ["SoftwareApplication", "Tool"], "input": ["http://edamontology.org/data_2977",
"http://edamontology.org/data_2976"] }'
expected_types:
- URL
marginality: Recommended
parent: ContainerRecipe
property: input
type: external
type_url: http://semanticscience.org/resource/SIO_000230
- bsc_description: ''
cardinality: ONE
controlled_vocab: '[EDAM:Topic](http://edamontology.org/topic_0003)'
description: Keywords or tags used to describe this content. Multiple entries in
a keywords list are typically delimited by commas.
example: '{ "@type": ["SoftwareApplication", "Tool"], "ketwords": "Biology, chemistry,
medicine" }'
expected_types:
- Text
marginality: Optional
parent: CreativeWork
property: keywords
type: ''
type_url: ''
- bsc_description: a list of keywords to describe the ContainerImage that the ContainerRecipe
will build
cardinality: MANY
controlled_vocab: ''
description: Keywords or tags used to describe this content. Multiple entries in
a keywords list are typically delimited by commas.
example: ''
expected_types:
- Text
marginality: Recommended
parent: CreativeWork
property: keywords
type: ''
type_url: ''
- bsc_description: Labels are a list of key, value pairs that encompass named metadata
for the container.
cardinality: MANY
controlled_vocab: ''
description: Labels are a list of key, value pairs that encompass named metadata
for the container.
example: ''
expected_types:
- Text
marginality: Recommended
parent: ContainerRecipe
property: labels
type: ''
type_url: ''
- bsc_description: The applicable software license. **Note:** Bioschemas have changed
the Expected Types to [URL](http://schema.org/URL) or [Text](http://schema.org/Text).
cardinality: MANY
controlled_vocab: ''
description: A license document that applies to this content, typically indicated
by URL.
example: '{ "@type": ["SoftwareApplication", "Tool"], "license": "https://www.apache.org/licenses/LICENSE-2.0"
}'
expected_types:
- Text
- URL
marginality: Recommended
parent: CreativeWork
property: license
type: ''
type_url: ''
- bsc_description: ''
cardinality: ONE
controlled_vocab: ''
description: The name of the item.
example: ''
expected_types:
- Text
marginality: Minimum
parent: Thing
property: name
type: ''
type_url: ''
- bsc_description: Operating systems on which the container runs, typically Linux
derivatives and (for some containers) Windows.
cardinality: MANY
controlled_vocab: ''
description: Operating systems supported (Windows 7, OSX 10.6, Android 1.6).
example: '{ "@type": ["SoftwareApplication", "Tool"], "operatingSystem": ["Windows
7 or above", "Linux/Unix", "Mac/Apple"] }'
expected_types:
- Text
marginality: Optional
parent: ContainerRecipe
property: operatingSystem
type: ''
type_url: ''
- bsc_description: The output format of the data. Must be one of the [EDAM:Data](http://edamontology.org/data_0006)
concept labels or one of its synonyms.
cardinality: MANY
controlled_vocab: '[EDAM:Data](http://edamontology.org/data_0006)'
description: 'The output format of the data'
example: '{ "@type": ["SoftwareApplication", "Tool"], "input": ["http://edamontology.org/data_1383",
"http://edamontology.org/data_1384"] }'
expected_types:
- URL
marginality: Recommended
parent: ContainerRecipe
property: output
type: external
type_url: http://semanticscience.org/resource/SIO_000229
- bsc_description: ''
cardinality: MANY
controlled_vocab: ''
description: The publisher of the creative work.
example: '{ "@type": ["SoftwareApplication", "Tool"], "publisher": { "@type":
"Organization", "name": "National Center for Biotechnology Information National
Library of Medicine", "alternatName": "NCBI NLM" } }'
expected_types:
- Organization
- Person
marginality: Recommended
parent: CreativeWork
property: publisher
type: ''
type_url: ''
- bsc_description: A documentation for the container, typically showing install steps,
usage, and a changelog.
cardinality: MANY
controlled_vocab: ''
description: Software application help.
example: '{ "@type": ["SoftwareApplication", "Tool"], "softwareHelp": "https://biotools.readthedocs.io/en/latest/"
}'
expected_types:
- CreativeWork
marginality: Optional
parent: ContainerRecipe
property: softwareHelp
type: ''
type_url: ''
- bsc_description: ''
cardinality: MANY
controlled_vocab: ''
description: 'Component dependency requirements for application. This includes runtime
environments and shared libraries that are not included in the application distribution
package, but required to run the application (Examples: DirectX, Java or .NET
runtime). Supersedes requirements.'
example: '{ "@type": ["SoftwareApplication", "Tool"], "softwareRequirements":
"JRE 8.0 or above" }'
expected_types:
- Text
- URL
marginality: Optional
parent: ContainerRecipe
property: softwareRequirements
type: ''
type_url: ''
- bsc_description: version of the container instance
cardinality: ONE
controlled_vocab: ''
description: Version of the software instance.
example: '{ "@type": ["SoftwareApplication", "Tool"], "softwareVersion": "1.0.2"
}'
expected_types:
- Text
marginality: Minimum
parent: ContainerRecipe
property: softwareVersion
type: ''
type_url: ''
- bsc_description: URL of the container, typically associated with a registry that
shows the ContainerRecipe, vulnerability scans, and other metadata. This property
can be used on a page listing many containers to indicate each individual container's
page.
cardinality: ONE
controlled_vocab: ''
description: URL of the item.
example: ''
expected_types:
- URL
marginality: Minimum
parent: Thing
property: url
type: ''
type_url: ''
name: ContainerRecipe
parent_type: SoftwareSourceCode
spec_info:
description: This Container profile specification presents a representation of a
Container such as Docker or Singularity that is used in scientific compute.
full_example: https://www.github.com/openschemas/spec-container
version: 0.0.1
version_date: 20181021T214745
spec_type: Profile
status: revision
subtitle: Bioschemas specification describing a Container (e.g., Docker, Singularity)
used in Scientific Compute
use_cases_url: https://www.github.com/openschemas/spec-container
version: 0.0.1
---
{% include head.html %}
{% include header.html %}
{% include navbar.html %}
{% include profile_start.html %}
{% include specification_table.html %}


RFC: 1833
Rpcbind can help us look for NFS-shares. So look out for nfs. Obtain list of services running with RPC:
rpcbind -p <IP_ADDRESS>


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Welcome¶
Pymusepipe is a Python package which serves as a wrapper around the main processing steps of the MUSE data reduction pipeline (Weilbacher et al. 2019). Pymusepipe includes a simple data organiser and prescriptions for the structure of the data files (but no database per se), a wrapper around the main functionalities of MUSE data reduction pipeline, accessed via EsoRex command-line recipes, to remove the instrumental signatures. Pymusepipe additionally provides a set of modules supporting the alignment, mosaicking, two-dimensional and three-dimensional convolution.
pymusepipe is also made for multi-pointing mosaics and multi-targets surveys as it will process targets automatically when provided a specific dictionary of which target and which pointings to consider.
A description of the pipeline and its usage to reduce data from the PHANGS-MUSE survey are presented in Emsellem et al. (2022)
Contact
The pymusepipe module is maintained by Eric Emsellem. contact via
Attention
Please do not forget to cite Emsellem et al. (2022) and the MUSE data reduction pipeline paper (Weilbacher et al. 2019) if you make use of pymusepipe in your work. In particular, we suggest you add the following text (or equivalent) to the data reduction section of your work.
The dataset was reduced using recipes the MUSE data processing pipeline software (Weilbacher et al. 2019). All recipes were executed with ESOREX commands, wrapped around using the dedicated python package pymusepipe (Emsellem et al. 2022).
GitHub Repository¶
You can access the source code of pymusepipe and its previous releases directly in its official GitHub repository https://github.com/emsellem/pymusepipe.


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After writing first email(or selecting it from suggestions), press comma and you'll see the
browser suggests you more and validates each email.


Daniel O'Donnell wants an affair on Coronation Street
by Ian BrennanNational heart-throb Daniel O'Donnell has claimed he is ready to have an affair...on Coronation Street.
The Donegal native spoke of his love for the British soap opera, his favourite show, and that he would love a cameo appearance.
However, a simple walk-on part or showing up in the background wouldn't be enough for the singer.
"I'd love an affair," he joked. "I could have had an affair with Mavis (Rita’s famously dithery sidekick), but Mavis has been gone for ages.
"Mary might be the only one left for me now, so maybe me and Mary in her campervan!
"I could be the mechanic! I’d love a cameo – I’d sweep the streets, anything. It’s a great programme."
O'Donnell also gave an insight to the Irish Daily Mirror into what his life is like outside of work, saying that he enjoys playing cards in his local town hall.
"Everybody knows me, this is where I was born and grew up. I’ve gone for the past 50 years.
"I love playing cards and we have great fun – we don’t take it seriously and we’re always laughing.
"Sometimes you can win a fiver or a tenner. We pay 5 Euros to go in and then the top prize is 15 Euros."


When you look at the UM Libraries (UML) homepage, you may not realize all the components at work behind the scenes. At first glance, you probably notice a nice, big search box that is used to find library materials, a header filled with buttons that help you navigate to other pages, the scrolling thumbnail images and headlines near the footer, to name few. What you don’t see are the several instances of WordPress, a tool to create blog posts and pages, that power the UML site.
One use of WordPress is to promote our UML news, including events, exhibitions, programs, and resources. As we embark on this redesign, we want to think about how we can share that news with our community more effectively. Since 2011, we have been sharing our news using the blogging capabilities offered by WordPress. While our WordPress site has successfully handled the integration of various systems that are vital to our end-users’ experience (e.g., the library catalog and subject guides), we’ve decided to align with University Communications and migrate our news stories from WordPress to their chosen platform for news-related online publications, which will be highly advantageous for us in reaching our audience.
UM Communications uses Cascade, another content management system, to run the University website and also as their news publishing platform. Since the launch of the University news site, two news subsites that showcase the School of Architecture, and the School of Nursing and Health Studies have gone live, with more in development.
By migrating to Cascade, UML news will appear on a subsite that will exclusively feature UML content and will be in dialogue with the University news site. Other advantages to creating a UML news site on Cascade include:
- Increased visibility of UML news items, across campus and the community-at-large
- Greater possibility/likelihood of UML news items being “picked up” or featured on the University’s news site
- Ability (via RSS) to identify pertinent news items from Cascade new sites and feature them on the forthcoming redesigned UML website
Next Steps
This migration project is part of the redesign process for the UML website. It will be managed primarily by members of the Web & Application Development and Library Communications teams, who will soon have the opportunity to receive training on how to use Cascade. We can start populating our news subsite with UML stories, aiming to go live on Cascade sometime this summer. We hope to develop a new workflow internally for creating, editing, and publishing content. Additionally, we hope to align our news content, structure, and stories with University goals and best practices.


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| WinAPI Wrapper |
This topic describes how to use IPtr<T> safe COM interface pointers.
In general you use the IPtr<T> pointer like a regular T* pointer, which means you can normally access interface functions using the -> operator. But the IPtr<T> simplifies pointer management.
The first thing you have to do in every COM program is the initialization of the COM library. Normally, you have to remember to uninitialize it on time, e.g. when you program ends. But you can also use the COMInitializer class which frees you from this responsibility:
int main() { COMInitializer _init(); /* ... COM code ... */ return 0; }
Note that if the scope in which the _init variable is defined ends (e.g. when you return from the function), the COM library is uninitialized in the destructor of the _init object.
The basic functionality of a COM program is creating of COM object instances. The following code creates an instance of the FilterGraph object from DirectShow.
IPtr<IGraphBuilder> p_graph( CLSID_FilterGraph );
The constructor of the pointer has also second argument, that defaults to __uuid(T), where T is the interface class (in this case IGraphBuilder, so the second argument finally resolves to IID_IGraphBuilder). The constructor has also other arguments (with their default values), that it passes to the CoCreateInstance function in order to create the object.
The following examples are all equivalent to the above code:
IPtr<IGraphBuilder> p_graph( CLSID_FilterGraph, IID_IGraphBuilder ); IPtr<IGraphBuilder> p_graph; p_graph.CreateInstance( CLSID_FilterGraph ); IPtr<IGraphBuilder> p_graph; CoCreateInstance( CLSID_FilterGraph, 0, CLSCTX_INPROC_SERVER, IID_IGraphBuilder, p_graph.GetVPP() );
The last, clumsy example follows the actual COM syntax, but it is fully equivalent to all three previous examples as all resolve to the same code when compiled.
Another important functionality in a COM application is obtaining interfaces to objects. With the IPtr<T> pointer it can be as simple as this:
IPtr<IMediaControl> p_control = p_graph;
The above code uses the p_graph pointer defined before. Because the filter graph exposes the IMediaControl interface, we can query for it. Either the constructor or the assignment operator, when fed with a non-matching pointer, call the IUnknown::QueryInterface function on the object to obtain an appropriate interface. Of course this may fail when the object does not support the interface, in which case we end up with a zero pointer. We can test for it like follows:
if ( ! p_control.IsValid() ) return;
Because all objects are gracefully released by the IPtr<T> destructor, we don't have to worry about the releasing when returning from a function or generally exiting a scope.
The following examples are equivalent to the first one:
IPtr<IMediaControl> p_control( p_graph ); IPtr<IMediaControl> p_control( p_graph, IID_IMediaControl ); IPtr<IMediaControl> p_control; p_control = p_graph; IPtr<IMediaControl> p_control; p_control.CreateInterface( p_graph ); IPtr<IMediaControl> p_control; p_graph->QueryInterface( IID_IMediaControl, p_control.GetVPP() );
And like before, the last example shows the most clumsy generic COM syntax.
Compare the following two examples - the first one created using regular COM syntax, the second one using the IPtr<T> safe pointer:
IMediaControl* p_control = 0; p_graph->QueryInterface( IID_IMediaControl, reinterpret_cast<void**>( &p_control ) ); p_control->Run(); p_control->Release(), p_control = 0; IPtr<IMediaControl>( p_graph )->Run();
Note that in both examples, the p_graph object can be either IGraphBuilder* or IPtr<IGraphBuilder>.
You don't have to worry if you are assigning IPtr<T> pointers. The safe pointer class uses reference counting, fundamental property of COM. Both of the following examples are thus valid and do not introduce any memory leaks:
{ IPtr<IMediaControl> ptr1( p_graph ); IPtr<IMediaControl> ptr2 = ptr1; } { IMediaControl* ptr1 = 0; p_graph->QueryInterface( IID_IMediaControl, reinterpret_cast<void**>( &ptr1 ) ); IPtr<IMediaControl> ptr2 = ptr1; ptr1->Release(), ptr1 = 0; }
In COM, the IUnknown::Release method decrements the reference counter of an object, and if the counter reaches zero the object is release. When using the IPtr<T> safe pointers, you don't have to worry about releasing pointers, because the destructor automatically releases them.
Sometimes you need to release a pointer before the current scope ends, i.e. before the IPtr<T>::~IPtr destructor is called. You can do this explicitly.
IPtr<IMediaControl> p_control = p_graph; /* ### do something ### */ // p_control.IsValid() == true p_control.Release(); // p_control.IsValid() == false /* ### do something else ### */
Explicitly releasing pointers automatically zeroes them.
Sometimes COM objects or interfaces are obtained in a different way, e.g. they are output from other objects' functions. You can handle this using the GetPP function (or GetVPP if void** is needed). The following function enumerates DirectShow filters of a specified category:
template< class Enumerator > void EnumerateDevices( REFCLSID category, Enumerator& enumerator ) { // Create device enumerator object IPtr<ICreateDevEnum> p_devenum( CLSID_SystemDeviceEnum, CLSCTX_INPROC ); // Create iterator object IPtr<IEnumMoniker> p_enum_iterator; p_devenum->CreateClassEnumerator( category, p_enum_iterator.GetPP(), 0 ); if ( ! p_enum_iterator.IsValid() ) return; // Enumerate subsequent devices IPtr<IMoniker> p_entry; while ( SUCCEEDED( p_enum_iterator->Next( 1, p_entry.GetPP(), 0 ) ) ) if ( enumerator( p_entry ) ) // break if enumerator has enough break; }


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Class Reflection
Class Reflection Reflection class is used to get reflection class variables and make accessible for callee
public static
object
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string/
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public
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#
getMethods( mixed $scope = null, boolean $inherit = false, null $static = null )
Get All methods of the reflection class, you are allowed to filter out methods you required. |
public
mixed
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public
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#
getClassMethods( mixed & $methods, mixed & $inherit, mixed & $key, mixed & $static, mixed & $return )
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$reflectionClass |
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$reflectionProperty |
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$methodScopes | [
'public' => [],
'protected' => [],
'private' => []
] |


Bridging Fiji and OpenMPI for Easy-to-Use Parallel Processing of Images
Outline
Gain access to remote HPC
Installation and configuration of the plugin
Upload, run and download macros remotely
Monitoring progress, output and error reporting
Parallelization of a Macro script
How to use the progress reporting functions
Demonstration of a real parallel macro
Advanced parallelization of processing on large images by use of MPI-aware Ops
Workshop structure
Each part of the workshop is hands-on.
The participants are expected to first attempt to work through the practical worksheets.
Follow the link in the presentation to open the worksheet.
The worksheets will then be presented and explained by the presenter.
Gain access to remote HPC
Gain access to remote HPC
Get training credentials
Installation and configuration of the plugin
Installation and configuration of the plugin
Installation:
How to install HPC Workflow Manager client
Installation and configuration of the plugin
Configuration:
How to set up the paradigm for Parallel Macro
Upload, run and download macros remotely
Upload, run and download macros remotely
Using HPC Workflow Manager to start a new Macro job
Monitoring progress, output and error reporting
Monitoring progress, output and error reporting
Monitoring progress, output and error reporting
Parallelization of a Macro script
Parallelization of a Macro script
How to write a parallel Macro
How to use the progress reporting functions
How to use the progress reporting functions
How to use the progress reporting functions
Demonstration of a real parallel macro
Demonstration of a real parallel macro
Demonstration of a real parallel macro
Advanced parallelization of processing on large images by use of MPI-aware Ops
Advanced parallelization of processing on large images by use of MPI-aware Ops
OpenMPI-enabled Fiji with scijava-parallel-mpi
Discussion
Discussion
Recap
Gain access to remote HPC
Installation and configuration of the plugin
Upload, run and download macros remotely
Monitoring progress, output and error reporting
Parallelization of a Macro script
How to use the progress reporting functions
Demonstration of a real parallel macro
Advanced parallelization of processing on large images by use of MPI-aware Ops
Thank you!
Advanced Remote Fiji Installation
How to install Parallel Macro on a remote cluster.


- Bio
- Education
- Experience
- AWS Software Development Engineer Apprentice
- Walt Disney Emerging Technology Intern
- US Army National Guard Radiology Technologist
- Goals
- Complete my CodeFellows training
- Complete the project assigned to me during OJT
- Be converted to a full-time employee in Amazon
- Fun Facts
- I am not a good singer but I like karaoke.
- I love foods!
- I enjoy outdoor activities like hiking and camping.
- I have visited 9 states so far. Aiming to visit all of it.
- I tried snowboarding for the first time.
- I had 4 dogs.
- I want to visit Japan, Korea, and Rome.
A military veteran. Originally from the Philippines.
Bachelor's of Science in Computer Science and Systems
University of Washington
Completion date: June 2018
The following are my professional experience:
My short-term and long-term goals I am working on:
Based from the guessing game:
Top 10 Movies (info courtesy of IMDB)


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This page tracks the new paper links made to our list of SIGGRAPH 2012 papers.
Page maintained by Ke-Sen Huang.
If you have additions or changes, send an e-mail.
February 24, 2013
- Naive Ray Tracing: A Divide-And-Conquer Approach
(TOG Paper)
January 17, 2013
- Image Melding: combining inconsistent images using patch-based synthesis
October 6, 2012
- Synthesizing Open Worlds with Constraints Using Locally Annealed Reversible Jump MCMC
September 26, 2012
- Printing Spatially-Varying Reflectance for Reproducing HDR Images
September 16, 2012
- Rig-Space Physics
- Manifold Exploration: A Markov Chain Monte Carlo technique for rendering scenes with difficult specular transport
September 4, 2012
- 3D Imaging Spectroscopy for Measuring Hyperspectral Patterns on Solid Objects
August 31, 2012
- Push it Real: Perceiving Causality in Virtual Interactions
- Render me Real? Investigating the Effect of Render Style on the Perception of Animated Virtual Humans
August 27, 2012
- Synthesizing Open Worlds with Constraints Using Locally Annealed Reversible Jump MCMC
- REVEL: Tactile Feedback Technology for Augmented Reality
August 23, 2012
- Diffusion Curve Textures for Resolution Independent Texture Mapping
August 20, 2012
August 12, 2012
- Mass Splitting for Jitter-Free Parallel Rigid Body Simulation
- Edge-Guided Resolution Enhancement in Projectors via Optical Pixel Sharing
- Position Correcting Tools for 2D Digital Fabrication
- Physical Face Cloning
- Coupled 3D Reconstruction of Sparse Facial Hair and Skin
August 6, 2012
- Practical Temporal Consistency for Image-Based Graphics Applications
August 4, 2012
- Guided Exploration of Physically Valid Shapes for Furniture Design
(
)
(
)
August 3, 2012
- Design Preserving Garment Transfer
(
)
(
)
(
)
July 30, 2012
- Object-Space Multiphase Implicit Functions
- Panorama Weaving: Fast and Flexible Seam Processing
July 29, 2012
- Design Preserving Garment Transfer
(
)
(
)
- Simple Formulas For Quasiconformal Plane Deformations
()
(
)
(TOG Paper)
- Reconstructing the Indirect Light Field for Global Illumination
- Deformable Objects Alive!
- Fabricating Articulated Characters from Skinned Meshes
- Mass Splitting for Jitter-Free Parallel Rigid Body Simulation
July 11, 2012
- Highlight Microdisparity for Improved Gloss Depiction
- Bilinear Spatiotemporal Basis Models
(TOG Paper)
July 9, 2012
- Panorama Weaving: Fast and Flexible Seam Processing
- Realistic Perspective Projections for Virtual Objects and Environments
(TOG Paper)
- Eyecatch: simulating visuomotor coordination for object interception
June 24, 2012
- Virtual Ray Lights for Rendering Scenes with Participating Media
()
(
)
- Interactive Images: Cuboid Proxies for Smart Image Manipulation
- Guided Exploration of Physically Valid Shapes for Furniture Design
(
)
(
)
- Primal-Dual Coding to Probe Light Transport
June 21, 2012
- Videoscapes: Exploring Sparse, Unstructured Video Collections
June 20, 2012
- Surface Flows for Image-Based Shading Design
(
)
(
)
- Motion-Driven Concatenative Synthesis of Cloth Sounds
- Topology-Adaptive Interface Tracking Using the Deformable Simplicial Complex
(TOG Paper)
June 17, 2012
- Functional Maps: A Flexible Representation of Maps Between Shapes
- Robust Modeling of Constant Mean Curvature Surfaces
- Bounded Distortion Mapping Spaces For Triangular Meshes
June 16, 2012
- Video-Based 3D Motion Capture through Biped Control
- Discovery of Complex Behaviors Through Contact-Invariant Optimization
- Fit and Diverse: Set Evolution for Inspiring 3D Shape Galleries
()
(
)
- Symmetry-Guided Texture Synthesis and Manipulation
(TOG Paper)
- Energy-based Self-Collision Culling for Arbitrary Mesh Deformations
- Steady Affine Motions and Morphs
SAMFeb2011.pdf
(TOG Paper)
- Plastic Trees: Interactive Self-Adapting Botanical Tree Models
-
-
-
June 4, 2012
- Printing Reflectance Functions
(TOG Paper)
- CrossShade: Shading Concept Sketches Using Cross-Section Curves
- DRAPE : DRessing Any PErson
(
(higher resolution))
(
)
- Sparse Zonal Harmonic Factorization for Efficient SH Rotation
(TOG Paper)
- Virtual Ray Lights for Rendering Scenes with Participating Media
()
()
- Guided Exploration of Physically Valid Shapes for Furniture Design
(
)
(
)
- Adaptive Manifolds for Real-Time High-Dimensional Filtering
- Symmetry-Guided Texture Synthesis and Manipulation
(TOG Paper)
- Interactive Images: Cuboid Proxies for Smart Image Manipulation
- Surface Flows for Image-Based Shading Design
- What makes Paris look like Paris?
May 29, 2012
May 24, 2012
- Ghost SPH for Animating Water
(
)
(
)
- Versatile Rigid-Fluid Coupling for Incompressible SPH
May 23, 2012
- Efficient Geometrically Exact Continuous Collision Detection
()
(
)
- K-Clustered Tensor Approximation: A Sparse Multi-Linear Model for Real-Time Rendering
(TOG Paper)
- Interactive spacetime control of deformable objects
- Dual Loops Meshing: Quality Quad Layouts on Manifolds
May 22, 2012
- Adaptive Image-based Intersection Volume
- Image Melding: combining inconsistent images using patch-based synthesis
- Tensor Displays: Compressive Light Field Synthesis using Multilayer Displays with Directional Backlighting
- 3D Imaging Spectroscopy for Measuring Hyperspectral Patterns on Solid Objects
May 20, 2012
- Variational mesh decomposition
(TOG Paper)
- Specular Reflection from Woven Cloth
(TOG Paper)
- High-Quality Image Deblurring With Panchromatic Pixels
(TOG Paper)
- A Theory of Monte Carlo Visibility Sampling
(TOG Paper)
- Naive Ray Tracing: A Divide-And-Conquer Approach
(TOG Paper)
- Fast Simulation of Skeleton-Driven Deformable Body Characters
(TOG Paper)
- Feature-Adaptive GPU Rendering of Catmull-Clark Subdivision Surfaces
(TOG Paper)
- Perceptual models of viewpoint preference
(TOG Paper)
- Fluid Simulation Using Laplacian Eigenfunctions
(TOG Paper)
- A Probabilistic Model for Component-Based Shape Synthesis
- Optimizing Locomotion Controllers Using Biologically-Based Actuators and Objectives
- An Algebraic Model for Parameterized Shape Editing
- Soft Body Locomotion
- Tools for Placing Cuts and Transitions in Interview Video
- Synthesis of Detailed Hand Manipulations Using Contact Sampling
- Tailored Displays to Compensate for Visual Aberrations
- Single-View Hair Modeling for Portrait Manipulation
- Precomputed Acceleration Noise for Improved Rigid-body Sound
- Image-Based Rendering for Scenes with Reflections
- Surface Flows for Image-Based Shading Design
- Understanding and Improving the Realism of Image Composites
- Design of Self-supporting Surfaces
- Point Sampling with General Noise Spectrum
- Selectively De-Animating Video
- Eulerian Video Magnification for Revealing Subtle Changes in the World
- Video Deblurring of Hand-held Cameras using Patch-based Synthesis
- Animating Bubble Interactions in a Liquid Foam
- Bidirectional Lightcuts
- Beady: Interactive Beadwork Design and Construction
- How do humans sketch objects?
- DRAPE : DRessing Any PErson
- Sketch-Based Shape Retrieval
May 18, 2012
- Videoscapes: Exploring Sparse, Unstructured Video Collections
- Decoupling Algorithms from Schedules for Easy Optimization of Image Processing Pipelines
May 13, 2012
- Tracking Surfaces with Evolving Topology
May 12, 2012
- Interactive Editing of Deformable Simulations
- Reflections on Simultaneous Impact
- Discrete Viscous Sheets
May 11, 2012
- Manifold Exploration: A Markov Chain Monte Carlo technique for rendering scenes with difficult specular transport
- Fast Automatic Skinning Transformations
- Robust Modeling of Constant Mean Curvature Surfaces
- Push it: Perceiving Causality in Virtual Interactions
- Render me Real? Investigating the Effect of Render Style on the Perception of Animated Virtual Humans
- Bounded Distortion Mapping Spaces For Triangular Meshes
- Dual Loops Meshing: Quality Quad Layouts on Manifolds
- Fields on Symmetric Surfaces
- Structure-aware Synthesis for Predictive Woven Fabric Appearance
- An Analytic Model for Full Spectral Sky-Dome Radiance
May 10, 2012
- Three-dimensional Proxies for Hand-drawn Characters
(TOG Paper)
- Bilinear Spatiotemopral Basis Models
(TOG Paper)
- Exploring Collections of 3D Models using Fuzzy Correspondences
- Stochastic Tomography and its Applications in 3D Imaging of Mixing Fluids
- PolyDepth: Real-time Penetration Depth Computation using Iterative Contact-Space Projection
(TOG Paper)
- Global parametrization by incremental flattening
- Interactive Sound Propagation using Compact Acoustic Transfer Operators
(TOG Paper)
- Interactive Sound Propagation using Compact Acoustic Transfer Operators
(TOG Paper)
- Continuous Character Control with Low-Dimensional Embeddings
(
)
(
)
- Gabor Noise by Example
- Lagrangian Vortex Sheets for Animating Fluids
- Ghost SPH for animating water
- MultiFLIP for Energetic Two-Phase Fluid Simulation
(TOG Paper)
- Schelling Points on 3D Surface Meshes
- Stitch Meshes for Modeling Knitted Clothing with Yarn-Level Detail
- HelpingHand: Example-Based Stroke Stylization
- Exploring Collections of 3D Models using Fuzzy Correspondences
(
)
(
)
- Underwater Rigid Body Dynamics
- Stress Relief: Improving Structural Strength of 3D Printable Objects
April 25, 2012
- Efficient Geometrically Exact Continuous Collision Detection
April 24, 2012
- Reflectance Model for Diffraction
(TOG Paper)
- What makes Paris look like Paris?
- PolyDepth: Real-time Penetration Depth Computation using Iterative Contact-Space Projection
(TOG Paper)
- Animation Cartography - Intrinsic Reconstruction of Shape and Motion
(TOG Paper)
- 3D Imaging Spectroscopy for Measuring Hyperspectral Patterns on Solid Objects
- Interactive spacetime control of deformable objects
- Interactive surface modeling using modal analysis
(TOG Paper)
April 18, 2012
- Guided Exploration of Physically Valid Shapes for Furniture Design
April 17, 2012
- Primal-Dual Coding to Probe Light Transport
April 16, 2012
- Reflectance Model for Diffraction
- Understanding and Improving the Realism of Image Composites
- Selectively De-Animating Video
- Underwater Rigid Body Dynamics
April 13, 2012
- Interactive Images: Cuboid Proxies for Smart Image Manipulation
- Design Preserving Garment Transfer
- Tensor Displays: Compressive Light Field Synthesis using Multilayer Displays with Directional Backlighting
- Eyecatch: simulating visuomotor coordination for object interception
April 11, 2012
- CrossShade: Shading Concept Sketches Using Cross-Section Curves
- Stochastic Tomography and its Applications in 3D Imaging of Mixing Fluids
- Reconstructing the Indirect Light Field for Global Illumination
April 10, 2012
- Manifold Exploration: A Markov Chain Monte Carlo technique for rendering scenes with difficult specular transport
- Single-View Hair Modeling for Portrait Manipulation
April 9, 2012
- Spacetime Expression Cloning for Blendshapes
- Interactive Editing of Deformable Simulations
- Beady: Interactive Beadwork Design and Construction
- Image Melding: combining inconsistent images using patch-based synthesis
- A Probabilistic Model for Component-Based Shape Synthesis
- Optimizing Locomotion Controllers Using Biologically-Based Actuators and Objectives
- An Algebraic Model for Parameterized Shape Editing
- Continuous Character Control with Low-Dimensional Embeddings
- Discrete Viscous Sheets
- Video Deblurring of Hand-held Cameras using Patch-based Synthesis
- Fit and Diverse: Set Evolution for Inspiring 3D Shape Galleries
- Theory, Analysis and Applications of 2D Global Illumination
(TOG Paper)
April 3, 2012
- Animating Bubble Interactions in a Liquid Foam
April 2, 2012
- Interactive Images: Cuboid Proxies for Smart Image Manipulation
- Guided Exploration of Physically Valid Shapes for Furniture Design
- Practical Temporal Consistency for Image-Based Graphics Applications
- A Framework for Content-Adaptive Photo Manipulation Macros: Application to Face, Landscape and Global Manipulations
(TOG Paper)
- Resolution Enhancement by Vibrating Displays
(
)
(
)
(TOG Paper)
April 1, 2012
- Structure-aware Synthesis for Predictive Woven Fabric Appearance
- Physically-Based Simulation of Rainbows
(TOG Paper)
- Continuous Penalty Forces
- Energy-based Self-Collision Culling for Arbitrary Mesh Deformations
- Precomputed Acceleration Noise for Improved Rigid-body Sound
- Point Sampling with General Noise Spectrum
- Self-supporting surfaces
- A Probabilistic Model for Component-Based Shape Synthesis
- Learning Hatching for Pen-and-Ink Illustration of Surfaces
(TOG Paper)
- How do humans sketch objects?
March 31, 2012
- Temporally Coherent Completion of Dynamic Shapes
(TOG Paper)
- Tracking Surfaces with Evolving Topology
- Micro Perceptual Human Computation
(TOG Paper)
- Fast High-Resolution Appearance Editing Using Superimposed Projections
(TOG Paper)
- Edge-Guided Resolution Enhancement in Projectors via Optical Pixel Sharing
- Plastic Trees: Interactive Self-Adapting Botanical
- Resolution Enhancement by Vibrating Displays
(TOG Paper)
- Image-Based Rendering for Scenes with Reflections
- Exploring Collections of 3D Models using Fuzzy Correspondences
- Simple Formulas For Quasiconformal Plane Deformations
(TOG Paper)
- Symmetry-Guided Texture Synthesis and Manipulation
(TOG Paper)
- K-Clustered Tensor Approximation: A Sparse Multi-Linear Model for Real-Time Rendering
(TOG Paper)
- Updated Sparse Cholesky Factors for Co-Rotational Elastodynamics
(TOG Paper)
- Exposing Photo Manipulation with Inconsistent Reflections
(TOG Paper)
March 26, 2012
- Video Stabilization using Epipolar Geometry
December 14, 2011
- Learning Hatching for Pen-and-Ink Illustration of Surfaces
- Physically-based Simulation of Rainbows
- On Filtering the Noise from the Random Parameters in Monte Carlo Rendering
- Robust Adaptive Photon Tracing using Photon Path Visibility
- VolCCD: Fast continuous collision culling between deforming volume meshes
kesen.huang@gmail.com


Welcome to my GitHub Page!
My Favorites Sites on the Internet
This is a link to YouTube!
This is a link to LinkedIn


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